Ozone pollution contributes to the yield gap for beans in Uganda, East Africa, and is co-located with other agricultural stresses

Air quality negatively impacts agriculture, reducing the yield of staple food crops. While measured data on African ground-level ozone levels are scarce, experimental studies demonstrate the damaging impact of ozone on crops. Common beans (Phaseolus vulgaris), an ozone-sensitive crop, are widely grown in Uganda. Using modelled ozone flux, agricultural surveys, and a flux-effect relationship, this study estimates yield and production losses due to ozone for Ugandan beans in 2015. Analysis at this scale allows the use of localised data, and results can be presented at a sub-regional level. Soil nutrient stress, drought, flood risk, temperature and deprivation were also mapped to investigate where stresses may coincide. Average bean yield losses due to ozone were 17% and 14% (first and second growing season respectively), equating to 184 thousand tonnes production loss. However, for some sub-regions, losses were up to 27.5% and other crop stresses also coincided in these areas. This methodology could be applied widely, allowing estimates of ozone impact for countries lacking air quality and/or experimental data. As crop productivity is below its potential in many areas of the world, changing agricultural practices to mitigate against losses due to ozone could help to reduce the crop yield gap.


EMEP model validation
When working with data from chemical transport models, it is important to ensure that modelled values are representing the atmosphere as adequately as possible, therefore (where possible) outputs are compared with measurements in a validation process.There is no established network of monitoring sites in Uganda as there are in some other African countries, for example South Africa.There have been a small number of studies looking at levels of air pollution in Uganda using low-cost sensing and measurement technologies.
For example, Kirenga et al. (2015) measured ambient ozone concentration in two of the largest cities in central Uganda (Kampala and Jinja) using real time monitors, for the period 30th June to 27th July 2014.Ozone concentrations were found to be low, below the WHO guideline levels of 50ppb eighthour mean, for example, the mean one-hour O3 concentration at the central monitoring site in Kampala measured over a period of seven days was 11.4 μg/m3 (5.8ppb).However, despite the authors placing monitoring devices in different land use types across the cities (commercial, industrial, residential), all the monitoring sites were in urban areas.Ozone concentrations are often considerably lower at urban sites compared to rural because there is a titration effect, with ozone being degraded by the compounds (NOx) by which it is also formed.This degradation occurs more often in cities than in rural areas, because there is more NO in cities.Therefore, to get a representative idea of ozone concentration across the country, monitoring sites need to be placed in both urban and rural areas.
More recently, Okure et al. (2022) deployed 120 diffusion tubes in Dec 2018 -Feb 2019 in selected urban centers in three of the four macro-regions in Uganda.Ozone concentrations varied spatially and with time, and two-monthly means ranged from 32.59ppb to 82.69ppb, with the highest values in South-Eastern Uganda, and values of >70ppb close to Kampala, and 50-60ppb at sites in central and Western Uganda.As expected, smaller urban centres and rural locations experienced higher O3 concentrations.The modelled data for 2015 in the current study showed the highest ozone concentration values in December (~62ppb) in South-Eastern and also central-Western areas of Uganda.Ozone concentration in a location does vary between years, due to changes in emissions and also shorter term due to weather conditions, so it is not surprising if values vary between the measured values in 2018/19 and the modelled values in 2015.
The EMEP model is used widely to estimate levels of air pollutants around the world.Modelled values have been validated using a variety of exercises.For example, Mills et al. (2018a), used the EMEP model to provide ozone concentration and flux data for the period 2010-12 to estimate the global impact of ozone on wheat production.The approach used to test the model's validity is explained in detail by the authors in the Supplementary Information of the paper.Over the course of a year, the EMEP model was found to capture the spatial and temporal variations in daily maximum hourly mean ozone concentration (Dmax) across the regions tested (Ireland, Germany, USA, Canada, Argentina, Japan) well, including the seasons with higher concentrations and longer ozone episodes.A very good correlation between the modelled and observed values (r 2 ranges from .88 to .95) was also found for model estimated Dmax and M7 (7-hour mean) for Global Atmosphere Watch (GAW) network sites in continents across the world, including some sites in Africa.
Similarly, Pandey et al. (2023) used the EMEP model to investigate ozone impacts on wheat production in India (for the period 2008-12).Again, a good overall performance of the EMEP model was found for most global sites from the GAW network.Unfortunately, there are no rural sites from India in the GAW network, however a good-quality dataset for model evaluation was available for one semi-urban site, Mohali (northern India).The model was found to generally overpredict the daily mean (Dmean) ozone levels (ca.58% over January to March, the POD3IAM accumulation period for most sites).However, daytime ozone (M7 and the 12-h daytime metric M12) was captured better than night-time concentrations.This implies greater certainty of modelled daytime ozone concentrations which are used to derive the POD3IAM values, but the lack of rural monitoring data meant that it wasn't possible to quantify the uncertainty for rural sites any further.
For Africa, unfortunately there are not many measured datasets available to validate the EMEP model estimates for ozone concentrations/flux.There were three African sites with measured ozone data for 2015 available in the GAW and Tropospheric Ozone Assessment Report (TOAR) observation portals.The monitoring sites were on mountain tops (Kenya and Canary Islands) and in a coastal location near Cape Town, South Africa (Fig. S1).The 2015 data for South Africa show that while there were some fluctuations, the general temporal pattern of ozone concentrations was similar between the modelled and observed data.For the purposes of this current study, it would be preferable to have monitored data from rural locations in Uganda/neighbouring countries.In Asia, Europe and North America, the primary source of ozone precursors is from vehicles and industry however in Africa, additional sources include household combustion and regional open biomass burning.Combustion from households is very difficult to calculate and for this work emissions estimates that have been compiled globally were used.Although very important, new emissions estimates have not been created as such activity was beyond the scope of the study.For the investigation of ozone impacts in Uganda, we used what was accepted as state of the art at the time when the calculations were performed.The emissions used in the model were derived from the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) model (2015, version 6a).For further information on the ECLIPSE model, see Stohl et al. (2015).
The weather data used to drive the EMEP model is from the Weather Research and Forecast (WRF) model.The outputs from this model (including wind speed, precipitation, temperature, relative humidity) have been validated in a number of studies using locations around the world, for example in Greece (Emmanouil et al., 2021) and Central India (Kadaverugu et al., 2021).In Africa, Mazzeo et al. (2022) tested WRF model estimates of temperature, relative humidity, wind speed and direction (while running with the Chimere chemical transport model, predicting PM2.5 levels) in Addis Ababa in Ethiopia, Nairobi in Kenya, and Kampala in Uganda.Results from the validation of the meteorological simulations performed over East African using WRF showed that the model is, on average, able to reproduce all four of the considered variables, providing values close to the observed data, with variable agreement between the three cities.When the WRF model is run with EMEP, the WRF model output is evaluated for surface temperature, relative humidity, and wind speed/direction using the National Oceanic and Atmospheric Administration (NOAA) dataset.For each region, the following equation was used to convert 2017 SPAM production data (IFPRI, 2020) to an estimate of 2015 bean production per 0.0833 grid cell:

Development of ozone flux-response relationship for bean
Experimental work was recently carried out to develop the first ozone flux-based relationship for common bean at the UKCEH solardome site in Bangor, North Wales, UK (Hayes et al., 2019).The aim of this experiment was to estimate ozone impacts on crops, including common bean, in Africa, particularly East Africa as bean production is high in this area.The final flux relationship included bean varieties grown in tropical countries, such as Mexico/South America (Tiger beans), India (Rajama) and African countries such as Kenya (Pinto, Mbombo, Turtle) and South Africa (Orca).No Ugandan beans were grown during this experiment as farmers use a large number of cultivars, some of which are very localised.However, Cannellini beans are white beans, which are grown in some areas of Uganda, and three of the seven cultivars are grown in neighbouring Kenya.We included all cultivars in the bean flux-response relationship in this study for Uganda as it is important to try and account for the possible differences in sensitivity/ozone response between cultivars, allowing for a more robust prediction of impact on yield.

Figure S2.
Relationship between the percentage bean yield and stomatal ozone flux (POD3IAM) for common bean.The relationship is based on experimental data collected in the UKCEH Bangor solardomes for seven bean cultivars during the growing seasons of 2017-2019.The regression of yield with POD3IAM followed the approach used by Fuhrer (1994).For each bean cultivar, yield was calculated at zero POD3IAM by obtaining the intercept value from the regression model.This was then used as the reference level for no effect when calculating relative yield ('Bean yield (%)' in this figure).
The ICP Vegetation, an international research programme investigating air pollution impacts on vegetation (including crops) has been building a crop sensitivity to ozone database, which is updated regularly with published experimental data on ozone impacts on crops.For beans, the majority of the datasets use 7-hour mean (M7) ozone concentration data.In the Uganda study, we use ozone flux, which has been shown to perform better than concentration-based models, providing improved predictions of the distribution of ozone damage.The LRTAP (Convention on Long-range Transboundary Air Pollution) Manual for Modelling and Mapping (Chpt.3)provides flux-based response relationships for a number of crops, (wheat, tomato and potato), however this is the first time that an ozone flux-response relationship has been developed for beans.
https://icpvegetation.ceh.ac.uk/sites/default/files/FinalnewChapter3v4Oct2017_000.pdfWhen the M7 bean data in the ozone sensitivity database are combined (Fig. S2), statistical analysis shows that the newer data (orange points) collected in the UKCEH Bangor solardomes (Hayes et al., 2019) and used for the ozone flux-relationship fit well with those already in the database, with the ozone sensitivity of these cultivars typical compared to the other legumes.Statistical analysis showed that while there was a strong relationship between 7-hour mean ozone and relative yield (p < 0.0001), there was no difference between the slopes for the two datasets (p = 0.46).(Peng et al., 2020).Temperature data is from WorldClim (https://www.worldclim.org/),CRU-TS 4.06 (Harris et al., 2020) downscaled with WorldClim 2.1 (Fick and Hijmans, 2017).Flood occurrence data are from Mitheu et al., 2023 -flood impact data were collated from four different data repositories from 2007 to 2015, and the number of years out of the 9 years considered when floods occurred were summed for each district in Uganda.

Figure S1 .
Figure S1.Hourly surface ozone concentrations measured for the year 2015 at Cape Point, South Africa, and modelled ozone concentrations from the EMEP-WRF Africa model.

Figure S3 .Figure S4 :
Figure S3.Legume (peas, beans and peanuts) data in the ozone sensitivity crop database from the ICP Vegetation spanning studies from 1988 to 2014, from locations in Europe, USA, Pakistan, India) using the 7-hour mean ozone metric (dark blue points).Data from the 2017-19 UKCEH solardome experiments using the 7 cultivars used in the Uganda study (including those growing in Africa) has also been included (orange points).

Figure S5 .
Figure S5.Flow chart of the methodology used in this study to estimate impacts of ozone on yield loss and production of common beans in Uganda.The EMEP model is the European Monitoring and Evaluation Programme chemical transport model (Simpson et al., 2012), which uses meteorological data from the Weather Research and Forecast (WRF) model.Within the EMEP model, the Deposition of O3 for Stomatal Exchange (DO3SE) model(Emberson et al., 2000) calculates ozone flux, using environmental data, including light, temperature and Vapour Pressure Deficit.Crop calendars were use from the Famine Early Warning Systems Network (FEWS NET) and the Food and Agriculture Organisation of the United Nations (FAO).The eastern sub-region of Karamoja has a unimodal growing season and was therefore included in the results separately from the main first and second growing seasons across the rest of the country.

Figure S6 .
Figure S6.Standardized Precipitation Evapotranspiration Index (SPEI), 6-month accumulation, monthly values for Uganda, for April to June 2015.The SPEI data values have been computed for Africa at 5km resolution, monthly, for the period 1981 to 2016 (Peng et al., 2020).

Table S4 -
regional conversion factors for beans (converting 2017 production values to 2015 values)

Table S5 .
Mills et al. (2018b)tress score for ozone, soil nutrient, deprivation, drought, temperature and flood occurrence.HWSD is the Harmonised World Soil Database and SPEI is the Standardised Precipitation-Evapotranspiration Index.: Soil nutrient data are from the Global Agro-Ecological Zones (GAEZ) data portal, derived from combinations of soil attributes, using data in the Harmonized World Soil Database (HWSD, v. 1.1, FAO/IIASA/ISRIC/ISS CAS/JRC 2009).The scoring system for soil nutrient stress (and % yield loss due to ozone) followMills et al. (2018b).The Deprivation Index is the Global Gridded Relative Deprivation Index (GRDI; v1), from NASA's Socioeconomic Data and Applications Centre (SEDAC) (CIESIN 2022).The SPEI data values have been computed for Africa at 5km resolution, monthly for the period 1981 to 2016 Note

Table S6 :
Estimated bean production per sub-region in Uganda, 2015, in tonnes (t), using 2017 SPAM data (IFPRI, 2020).The breakdown for each growing season (S1 and S2) is also given, per sub-region.

Table S7 .
Production totals for beans inUganda, in tonnes, for 2008Uganda, in tonnes, for  /9, 2015Uganda, in tonnes, for  , 2017Uganda, in tonnes, for   and 2018.The values for 2017 have been taken from the Spatial Production Allocation Model (SPAM) dataset, and those for 2015 have been estimated using the 2017 SPAM dataset and conversion factors per region, based on regional data for production from the 2008/9 and 2018 country-wide agricultural surveys.Survey totals for 2008/9 and 2018 are also given for comparison.Note, the 2008/9 survey data did not provide data at the sub-regional level.The FAOSTAT (https://www.fao.org/faostat/en/)total production values for Ugandan beans are also shown.